Maldeb Datasethttps://doi.org/10.34820/FK2/HQYV4XISMAIL, SETIA JULI IRZALTelkom University Dataverse2024-03-282024-03-28T06:58:49ZMalware-benign Image representation. The Dataset were collected from several malware repositories, including TekDefense, TheZoo, The Malware-Repo, Malware Database amd Malware Bazar. The benign samples were collected from Microsoft 10 and 11 system apps and several open source software repository including CNET, Sourceforge, FileForum, PortableFreeware. The samples were validated by scanning them using Virustotal Malware scanning services. The Samples underwent preprocessing by converting the malware binary into grayscale images following rules from Nataraj (2011). Nataraj Paper: https://vision.ece.ucsb.edu/research/signal-processing-malware-analysis. Maldeb Dataset is collected by Debi Amalia Septiyani and Halimul Hakim Khairul D. A. Septiyani, “Generating Grayscale and RGB Images dataset for windows PE malware using Gist Features extaction method,” Institut Teknologi Bandung, 2022, and Dani Agung Prastiyo, "Design and implementation of a machine learning-based malware classification system with an audio signal feature Analysis Approach," Institut Teknologi Bandung, 2023. The complete dataset can be accessed on this link https://ieee-dataport.org/documents/maldeb-dataset and https://github.com/julismail/Self-SupervisedComputer and Information SciencemalwareMalSSL - Self-Supervised Learning for Accurate and Label-efficient malware ClassificaitionJULI IRZAL ISMAIL, SETIADebi Amalia SeptiyaniHalimul Hakim KhairulDani Agung Prastiyo2024-03-28CC0CC0 Waiver